The debate between current versions of covariation and mechanism approaches to causal inference

Newsome, George L.

doi:10.1080/0951508032000067725

Cited by 18 publications

(21 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As mentioned at the start of the article, psychological theories about causal induction have traditionally fallen into two camps (Newsome, 2003): Covariation-based approaches characterize human causal induction as the consequence of a domain-general statistical sensitivity to covariation between cause and effect (e.g., Cheng & Novick, 1990Shanks & Dickinson, 1987), whereas mechanism-based approaches focus on the role of prior knowledge about the mechanisms by which causal force can be transferred (e.g., Ahn & Kalish, 2000;Shultz, 1982b;White, 1995). Recently, these two approaches have begun to be brought together through theories that consider how prior knowledge can be combined with covariational evidence (Koslowski, 1996;Waldmann, 1996;.…”

Section: Causal Mechanisms and Causal Theoriesmentioning

confidence: 99%

“…This history is marked by a tension between statistical learning and abstract prior knowledge about causality as accounts of human causal induction. Psychological theories about causal induction have tended to emphasize one of these two factors over the other (Cheng, 1997;Newsome, 2003;Shultz, 1982b): In the tradition of Hume (1739/1978), covariation-based approaches characterize human causal induction as the consequence of a domain-general statistical sensitivity to covariation between cause and effect (e.g., Cheng & Novick, 1990Shanks & Dickinson, 1987), whereas, in a tradition often traced to Kant (1781Kant ( /1964 see Shultz, 1982b, for an account of the connection), mechanismbased approaches focus on the role of prior knowledge about the mechanisms by which causal force can be transferred (e.g., Ahn & Kalish, 2000;Shultz, 1982b;White, 1995).Recently, explanations of human causal learning have begun to explore a middle ground between these positions, looking at how mechanism knowledge might influence learning from covariation between cause and effect (e.g., Lagnado & Sloman, 2004;Waldmann, 1996;. These accounts are based on a range of results indicating the importance of both of these factors.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Theory-based causal induction.

Griffiths¹,

Tenenbaum²

2009

Psychological Review

310

271

View full text Add to dashboard Cite

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the co-occurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge-identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computational-level analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domain-general statistical inference guided by domain-specific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge-the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships-and show how they provide the constraints that people need to induce useful causal models from sparse data. Keywords: causal induction, intuitive theories, rational analysis, Bayesian modelingIn 1695, Sir Edmond Halley was computing the orbits of a set of comets for inclusion in Newton's Principia Mathematica when he noticed a surprising regularity: The comets of 1531, 1607, and 1682 took remarkably similar paths across the sky, and visited the Earth approximately 76 years apart. Newton had already shown that comets should follow orbits corresponding to conic sectionsparabolas, hyperbolas, and ellipses-although no elliptical orbits had yet been observed. Halley inferred that the sightings of these comets were not three independent events, but three consequences of a single common cause: a comet that had visited the Earth three times, travelling in an elliptical orbit. He went on to predict that it would return along the same orbit in 1758. The comet returned as predicted, and has continued to visit the Earth approximately every 76 years since, providing a sensational confirmation of Newton's physics.Halley's discovery is an example of causal induction: inferring causal structure from data. Explaining this discovery requires appealing to two factors: abstract prior knowledge, in the form of a causal theory, and statistical inference. The prior knowledge that guided Halley was the mathematical theory of physics laid out by Newton. This theory identified the entities and properties relevant to understanding a physical system, formalizing notions such as velocity and acceleration, and characterized the relations that can ho...

show abstract

Section: Causal Mechanisms and Causal Theoriesmentioning

confidence: 99%

mentioning

confidence: 99%

Theory-based causal induction.

Griffiths¹,

Tenenbaum²

2009

Psychological Review

310

271

View full text Add to dashboard Cite

show abstract

“…Newsome 2003;Steel 2004). Once set in motion, a mechanism or conjuncture of mechanisms will always produce outcome Y.…”

Section: Multiple Causal Pathsmentioning

confidence: 98%

Conjunctural causation in comparative case-oriented research

Aus

2007

Qual Quant

View full text Add to dashboard Cite

show abstract

“…L'intérêt pour ce type de raisonnement est tout à fait justifié, certains auteurs allant jusqu'à prétendre que, à l'exception des mathématiques et de la logique, la capacité d'apprécier des liens de causalité est fondamentale dans toutes nos activités de raisonnement et de résolution de problèmes (Newsome, 2003). Par exemple dans nos activités quotidiennes, lorsque nous nous questionnons sur les aliments mal digérés ou les exercices qui nous donnent un mal de dos, nous envisageons continuellement des liens de causalité potentiels.…”

unclassified

“…Par exemple, Greville et Buehner constatent que la cote de force causale (d'un rayon susceptible de tuer des bactéries) attribuée à un même ensemble de conjonctions diminue au fur et à mesure que le délai entre la cause et l'effet augmente. D'autres études révèlent l'incidence de certaines connaissances a priori sur la force causale attribuée, tel que le délai habituel attendu entre la cause et l'effet (Buehner & May, 2002, 2003, 2004Hagmayer & Waldmann, 2002), et la connaissance d'un mécanisme causal plausible.…”

unclassified

Quand des mécanismes génératifs et préventifs rencontrent des informations compatibles et incompatibles dans le raisonnement causal probabiliste.

Desrochers

Walsh²,

Sacy³

2012

Canadian Journal of Experimental Psychology / Revue canadienne

View full text Add to dashboard Cite

Several recent models of probabilistic causal reasoning in adults propose the existence of multiple interactions between ascending and descending factors. The aim of the present study is to evaluate the potential interactions between knowledge about generative and preventive mechanisms, the delta p of the data, and the relative importance given to the type of data provided. Two experiments involving 54 participants each are conducted, in which participants are invited to quantify the nature of a potential link (causal or associative) between adding a chemical substance to the asphalt of the roads and the formation of a slippery road in the winter, after being given information suggestive of (1) a generative mechanism, (2) a preventive mechanism, or (3) nothing special. Results show an influence of the suggested mechanisms on the reading of data that were provided, especially those with a delta p that is compatible with the a priori mechanism. These results are interpreted and discussed in line with the importance of considering multiple factors in probabilistic causal reasoning. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

show abstract

The debate between current versions of covariation and mechanism approaches to causal inference

Cited by 18 publications

References 23 publications

Theory-based causal induction.

Theory-based causal induction.

Conjunctural causation in comparative case-oriented research

Quand des mécanismes génératifs et préventifs rencontrent des informations compatibles et incompatibles dans le raisonnement causal probabiliste.

Contact Info

Product

Resources

About